Handbook of machine learning (Record no. 35250)

MARC details
000 -LEADER
fixed length control field 06485cam a2200277 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CUTN
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20210806151226.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180530m20199999njua b 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789813271227 (hc : alk. paper : v. 1)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9813271221 (hc : alk. paper : v. 1)
041 ## - LANGUAGE CODE
Language English
042 ## - AUTHENTICATION CODE
Authentication code pcc
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 23
Item number MAR
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Marwala, Tshilidzi,
245 10 - TITLE STATEMENT
Title Handbook of machine learning
Statement of responsibility, etc Tshilidzi Marwala (University of Johannesburg, South Africa).
300 ## - PHYSICAL DESCRIPTION
Extent volumes :
Other physical details illustrations ;
Dimensions 25 cm
505 0# - FORMATTED CONTENTS NOTE
Contents volume 1. Foundation of artificial intelligence --
Title Contents<br/>Preface<br/>About the Author<br/>Acknowledgements<br/>1. Introduction<br/>1.1 Introduction<br/>1.2 Time Domain Data<br/>1.2.1 Average<br/>1.2.2 Variance<br/>1.2.3 Kurtosis<br/>1.3 Frequency Domain<br/>1.4 Time–Frequency Domain<br/>1.5 Fractals<br/>1.6 Stationarity<br/>1.7 Common Mistakes on Handling Data<br/>1.8 Outline of the Book<br/>1.9 Conclusions<br/>References<br/>2. Multi-layer Perceptron<br/>2.1 Introduction<br/>2.2 Multi-layer Perceptron<br/>2.3 Training the Multi-layered Perceptron<br/>2.4 Back-propagation Method<br/>2.5 Scaled Conjugate Method<br/>2.6 Multi-layer Perceptron Classifier<br/>2.7 Applications to Economic Modelling<br/>2.8 Application to a Steam Generator<br/>2.9 Application to Cylindrical Shells<br/>2.10 Application to Interstate Conflict<br/>2.11 Conclusions<br/>References<br/>3. Radial Basis Function<br/>3.1 Introduction<br/>3.2 Radial Basis Function<br/>3.3 Model Selection<br/>3.4 Application to Interstate Conflict<br/>3.5 Call Behaviour Classification<br/>3.6 Modelling the CPI<br/>3.7 Modelling Steam Generator<br/>3.8 Conclusions<br/>References<br/>4. Automatic Relevance Determination<br/>4.1 Introduction<br/>4.2 Mathematical Basis of the Automatic Relevance Determination<br/>4.2.1 Neural networks<br/>4.2.2 Bayesian framework<br/>4.2.3 Automatic relevance determination<br/>4.3 Application to Interstate Conflict<br/>4.4 Applications of ARD in Inflation Modelling<br/>4.5 Conclusions<br/>References<br/>5. Bayesian Networks<br/>5.1 Introduction<br/>5.2 Neural Networks<br/>5.3 Hybrid Monte Carlo<br/>5.4 Shadow Hybrid Monte Carlo (SHMC) Method<br/>5.5 Separable Shadow Hybrid Monte Carlo<br/>5.6 Comparison of Sampling Methods<br/>5.7 Interstate Conflict<br/>5.8 Conclusions<br/>References<br/>6. Support Vector Machines<br/>6.1 Introduction<br/>6.2 Support Vector Machines for Classification<br/>6.3 Support Vector Regression<br/>6.4 Conflict Modelling<br/>6.5 Steam Generator<br/>6.6 Conclusions<br/>References<br/>7. Fuzzy Logic<br/>7.1 Introduction<br/>7.2 Fuzzy Logic Theory<br/>7.3 Neuro-fuzzy Models<br/>7.4 Steam Generator<br/>7.5 Interstate Conflict<br/>7.6 Conclusions<br/>References<br/>8. Rough Sets<br/>8.1 Introduction<br/>8.2 Rough Sets<br/>8.2.1 Information system<br/>8.2.2 The indiscernibility relation<br/>8.2.3 Information table and data representation<br/>8.2.4 Decision rules induction<br/>8.2.5 The lower and upper approximation of sets<br/>8.2.6 Set approximation<br/>8.2.7 The reduct<br/>8.2.8 Boundary region<br/>8.2.9 Rough membership functions<br/>8.3 Discretization Methods<br/>8.3.1 Equal-width-bin (EWB) partitioning<br/>8.3.2 Equal-frequency-bin (EFB) partitioning<br/>8.4 Rough Set Formulation<br/>8.5 Rough Sets vs. Fuzzy Sets<br/>8.6 Multi-layer Perceptron Model<br/>8.7 Neuro-rough Model<br/>8.7.1 Bayesian training on rough sets<br/>8.7.2 Markov Chain Monte Carlo (MCMC)<br/>8.8 Modelling of HIV<br/>8.9 Application to Modelling the Stock Market<br/>8.10 Interstate Conflict<br/>8.11 Conclusions<br/>References<br/>9. Hybrid Machines<br/>9.1 Introduction<br/>9.2 Hybrid Machine<br/>9.2.1 Bayes optimal classifier<br/>9.2.2 Bayesian model averaging<br/>9.2.3 Bagging<br/>9.2.4 Boosting<br/>9.2.5 Stacking<br/>9.2.6 Evolutionary machines<br/>9.3 Theory of Hybrid Networks<br/>9.3.1 Equal weights<br/>9.3.2 Variable weights<br/>9.4 Condition Monitoring<br/>9.5 Caller Behaviour<br/>9.6 Conclusions<br/>References<br/>10. Auto-associative Networks<br/>10.1 Introduction<br/>10.2 Auto-associative Networks<br/>10.3 Principal Component Analysis<br/>10.4 Missing Data Estimation<br/>10.5 Genetic Algorithm(GA)<br/>10.6 Machine Learning<br/>10.7 Modelling HIV<br/>10.8 Artificial Beer Taster<br/>10.9 Conclusions<br/>References<br/>11. Evolving Networks<br/>11.1 Introduction<br/>11.2 Machine Learning<br/>11.3 Genetic Algorithm<br/>11.4 Learn++ Method<br/>11.5 Incremental Learning Method Using Genetic Algorithm (ILUGA)<br/>11.6 Optical Character Recognition (OCR)<br/>11.7 Wine Recognition<br/>11.8 Financial Analysis<br/>11.9 Condition Monitoring of Transformers<br/>11.10 Conclusions<br/>References<br/>12. Causality<br/>12.1 Introduction<br/>12.2 Correlation<br/>12.3 Causality<br/>12.4 Theories of Causality<br/>12.4.1 Transmission theory of causality<br/>12.4.2 Probability theory of causality<br/>12.4.3 Projectile theory of causality<br/>12.4.4 Causal calculus and structural learning<br/>12.4.5 Granger causality<br/>12.4.6 Structural learning<br/>12.4.7 Manipulation theory<br/>12.4.8 Process theory<br/>12.4.9 Counter factual theory<br/>12.4.10 Neyman–Rubin causal model<br/>12.4.11 Causal calculus<br/>12.4.12 Inductive causation (IC)<br/>12.5 How to Detect Causation?<br/>12.6 Causality and Artificial Intelligence<br/>12.7 Causality and Rational Decision<br/>12.8 Conclusions<br/>References<br/>13. Gaussian Mixture Models<br/>13.1 Introduction<br/>13.2 Gaussian Mixture Models<br/>13.3 EM Algorithm<br/>13.4 Condition Monitoring: Transformer Bushings<br/>13.5 Condition Monitoring: Cylindrical Shells<br/>13.6 Condition Monitoring: Bearings<br/>13.7 Conclusions<br/>References<br/>14. Hidden Markov Models<br/>14.1 Introduction<br/>14.2 Hidden Markov Models<br/>14.3 Condition Monitoring: Motor Bearing Faults<br/>14.4 Speaker Recognition<br/>14.5 Conclusions<br/>References<br/>15. Reinforcement Learning<br/>15.1 Introduction<br/>15.2 Reinforcement Learning: TD-Lambda<br/>15.3 Game Theory<br/>15.4 Multi-agent Systems<br/>15.5 Modelling the Game of Lerpa<br/>15.6 Modelling of Tic–Tac–Toe<br/>15.7 Conclusions<br/>References<br/>16. Conclusion Remarks<br/>16.1 Summary of the Book<br/>16.2 Implications of Artificial Intelligence<br/>References<br/>Index
520 ## - SUMMARY, ETC.
Summary, etc This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Reference Books
100 1# - MAIN ENTRY--PERSONAL NAME
Dates associated with a name 1971-
Relator term author.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 7
b cbc
c orignew
d 1
e ecip
f 20
g y-gencatlg
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Location Shelving location Date of Cataloging Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction CUTN Central Library CUTN Central Library Reference 06/07/2021   006.31 MAR 44010 06/07/2021 06/07/2021 Reference Books

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